FinanceFeeds

Crypto Machine Learning Algorithms Explained for Beginners

Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions ...
GitHub

[PUT ICML 2025] Accurate Parameter-Efficient Test-Time Adaptation for Time Series Forecasting

Real-world time series often exhibit a non-stationary nature, degrading the performance of pre-trained forecasting models. Test-Time Adaptation (TTA) addresses this by adjusting models during ...
GitHub

Toto - Time Series Optimized Transformer for Observability

Toto is a foundation model for multivariate time series forecasting with a focus on observability metrics. This model leverages innovative architectural designs to efficiently handle the ...
IEEE

A Data-Level Augmentation Framework for Time Series Forecasting With Ambiguously Related Source Data

Abstract: Many practical time series forecasting (TSF) tasks are plagued by data limitations. To alleviate this challenge, we design a data-level augmentation framework. It involves a time series ...
9to5Mac

iOS 26.2 added new AirDrop upgrade, here’s how it works

AirDrop has long been a highly popular iPhone feature, offering an easy way to share photos, files, and more with friends and family. But in iOS 26.2, there’s a new AirDrop enhancement available: ...
Frontiers

Artificial intelligence in financial market prediction: advancements in machine learning for stock price forecasting

This study reviews the advancements in AI-driven methods for predicting stock prices, tracing their evolution from traditional approaches to modern finance. The role of AI in the market extends beyond ...
IEEE

SparseTSF: Lightweight and Robust Time Series Forecasting via Sparse Modeling

Abstract: This paper introduces SparseTSF, a novel and extremely lightweight method for Long-term Time Series Forecasting (LTSF), designed to address the challenges of modeling complex temporal ...